depend on the resources of dependencies with `yield`.

For example, instead of using the same database session, you would create a new database session inside of the background task, and you would obtain the objects from the database using this new session. And then instead of passing the object from the database as a parameter to the background task function, you would pass the ID of that object and then obtain the object again inside the background task function....

import jcifs.internal.smb2.rdma.RdmaMemoryRegion;

/**
 * DiSNI memory region implementation.
 *
 * This class would integrate with DiSNI to provide registered
 * memory regions for high-performance RDMA operations.
 *
 * Note: This is a skeleton implementation. A real implementation would
 * require proper DiSNI integration with actual memory registration.
 */
public class DisniMemoryRegion extends RdmaMemoryRegion {

Now, let's see how you could use that to return a custom response.

Let's say that you want to return an <a href="https://en.wikipedia.org/wiki/XML" class="external-link" target="_blank">XML</a> response.

{* ../../docs_src/openapi_webhooks/tutorial001_py39.py hl[9:13,36:53] *}

The webhooks that you define will end up in the **OpenAPI** schema and the automatic **docs UI**.

/// info

The `app.webhooks` object is actually just an `APIRouter`, the same type you would use when structuring your app with multiple files.

///

    * You wouldn't write an application in Uvicorn directly. That would mean that your code would have to include more or less, at least, all the code provided by Starlette (or **FastAPI**). And if you did that, your final application would have the same overhead as having used a framework and minimizing your app code and bugs.

If you use a `requirements.txt` file you could specify the version with:

```txt
fastapi[standard]==0.112.0
```

that would mean that you would use exactly the version `0.112.0`.

Or you could also pin it with:

```txt
fastapi[standard]>=0.112.0,<0.113.0
```

If it was in a type annotation we could have used the vertical bar, as:

```Python
some_variable: PlaneItem | CarItem
```

But if we put that in the assignment `response_model=PlaneItem | CarItem` we would get an error, because Python would try to perform an **invalid operation** between `PlaneItem` and `CarItem` instead of interpreting that as a type annotation.

#### A "professional" attack { #a-professional-attack }

Of course, the attackers would not try all this by hand, they would write a program to do it, possibly with thousands or millions of tests per second. And they would get just one extra correct letter at a time.

But doing that, in some minutes or hours the attackers would have guessed the correct username and password, with the "help" of our application, just using the time taken to answer.

You could load it at the top level of the module/file, but that would also mean that it would **load the model** even if you are just running a simple automated test, then that test would be **slow** because it would have to wait for the model to load before being able to run an independent part of the code.

This way, you don't have to know beforehand what the valid field/attribute names are (as would be the case with Pydantic models).

This would be useful if you want to receive keys that you don't already know.

---

Another useful case is when you want to have keys of another type (e.g., `int`).

That's what we are going to see here.

In this case, you would accept any `dict` as long as it has `int` keys with `float` values: